National and Subnational estimates for Russia

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Russia. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively (see Methods or our paper for further explanation).

Table of Contents


Using data available up to the: 2020-06-21

Note that it takes time for infection to cause symptoms, to get tested for SARS-CoV-2 infection, for a positive test to return and ultimately to enter the case data presented here. In other words, today’s case data are only informative of new infections about two weeks ago. This is reflected in the plots below, which are by date of infection.

Expected daily confirmed cases by region


Figure 1: The results of the latest reproduction number estimates (based on estimated confirmed cases with a date of infection on the 2020-06-09) in Russia, stratified by region, can be summarised by whether confirmed cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively (see the methods for details). Regions with fewer than 40 confirmed cases reported on a single day are not included in the analysis (light grey).

National summary

Summary (estimates as of the 2020-06-09)

Table 1: Latest estimates (as of the 2020-06-09) of the number of confirmed cases by date of infection, the expected change in daily confirmed cases, the effective reproduction number, the doubling time (when negative this corresponds to the halving time), and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.
Estimate
New confirmed cases by infection date 8808 (8323 – 9288)
Expected change in daily cases Increasing
Effective reproduction no. 1 (1 – 1)
Doubling/halving time (days) 120 (66 – 920)
Adjusted R-squared 0.61 (0.26 – 0.98)

Confirmed cases, their estimated date of infection, and time-varying reproduction number estimates


Figure 2: A.) Confirmed cases by date of report (bars) and their estimated date of infection. B.) Time-varying estimate of the effective reproduction number. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Estimates from existing data are shown up to the 2020-06-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Time-varying rate of growth and doubling time


Figure 3: A.) Time-varying estimate of the rate of growth, B.) Time-varying estimate of the doubling time in days (when negative this corresponds to the halving time), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates from existing data are shown up to the 2020-06-09. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Regional Breakdown

Data availability

Limitations

Summary of latest reproduction number and confirmed case count estimates by date of infection


Figure 4: Confirmed cases with date of infection on the 2020-06-09 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily confirmed cases and shaded based on the expected change in daily confirmedcases. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.

Reproduction numbers over time in the six regions expected to have the most new confirmed cases


Figure 5: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-06-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.

Confirmed cases and their estimated date of infection in the six regions expected to have the most new confirmed cases


Figure 6: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-06-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Reproduction numbers over time in all regions


Figure 7: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-06-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.

Confirmed cases and their estimated date of infection in all regions

Figure 8: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-06-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Latest estimates (as of the 2020-06-09)

Table 2: Latest estimates (as of the 2020-06-09) of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time (when negative this corresponds to the halving time) in each region. The mean and 90% credible interval is shown.
Region New confirmed cases by infection date Expected change in daily cases Effective reproduction no. Doubling/halving time (days)
Adygea Republic 37 (24 – 47) Unsure 1 (0.8 – 1.2) -100 (12 – -9.7)
Altai Krai 72 (57 – 88) Unsure 1 (0.9 – 1.2) 100 (13 – -18)
Amur Oblast 45 (33 – 56) Unsure 1.1 (0.9 – 1.3) 120 (11 – -13)
Arkhangelsk Oblast 116 (96 – 133) Unsure 1 (0.9 – 1.1) -190 (22 – -18)
Astrakhan Oblast 62 (46 – 75) Unsure 1 (0.8 – 1.1) -120 (16 – -13)
Bashkortostan Republic 66 (50 – 81) Likely decreasing 0.9 (0.7 – 1) -22 (47 – -8.9)
Belgorod Oblast 78 (60 – 93) Unsure 1 (0.9 – 1.2) 98 (13 – -19)
Bryansk Oblast 87 (70 – 103) Unsure 1 (0.8 – 1.1) -85 (21 – -14)
Buryatia Republic 53 (38 – 65) Unsure 1 (0.8 – 1.2) -77 (17 – -11)
Chechen Republic 20 (10 – 27) Unsure 1 (0.7 – 1.4) 120 (7 – -8.3)
Chelyabinsk Oblast 151 (124 – 171) Likely increasing 1.1 (1 – 1.2) 34 (13 – -56)
Chuvashia Republic 76 (59 – 91) Unsure 1 (0.8 – 1.1) -100 (19 – -14)
Dagestan Republic 93 (73 – 109) Likely decreasing 0.9 (0.8 – 1.1) -42 (31 – -12)
Ingushetia Republic 38 (25 – 49) Unsure 1 (0.8 – 1.2) 96 (9.7 – -12)
Irkutsk Oblast 186 (164 – 212) Unsure 1.1 (0.9 – 1.2) 51 (16 – -46)
Ivanovo Oblast 91 (72 – 106) Unsure 0.9 (0.8 – 1.1) -35 (34 – -12)
Kabardino-Balkarian Republic 80 (63 – 96) Unsure 1 (0.9 – 1.2) 180 (15 – -18)
Kaliningrad Oblast 37 (24 – 47) Unsure 1 (0.8 – 1.2) -69 (13 – -9.5)
Kalmykia Republic 13 (5 – 19) Unsure 1 (0.6 – 1.4) -170 (6.4 – -6.1)
Kaluga Oblast 79 (60 – 94) Unsure 1 (0.8 – 1.1) -160 (17 – -14)
Kamchatka Krai 46 (33 – 58) Unsure 1 (0.8 – 1.2) -1600 (12 – -12)
Karachay-Cherkess Republic 96 (77 – 113) Likely increasing 1.1 (0.9 – 1.2) 33 (11 – -35)
Karelia Republic 35 (22 – 46) Likely increasing 1.2 (0.9 – 1.5) 17 (6.1 – -23)
Kemerovo Oblast 34 (22 – 43) Unsure 1 (0.8 – 1.2) -61 (13 – -9.4)
Khabarovsk Krai 73 (55 – 88) Unsure 1 (0.9 – 1.2) 74 (13 – -19)
Khakassia Republic 34 (23 – 45) Likely increasing 1.1 (0.9 – 1.4) 20 (6.8 – -20)
Khanty-Mansi Autonomous Okrug 219 (193 – 242) Increasing 1.1 (1 – 1.2) 21 (12 – 100)
Kirov Oblast 47 (32 – 59) Likely decreasing 0.9 (0.7 – 1.1) -26 (24 – -8.4)
Komi Republic 103 (83 – 119) Likely increasing 1.1 (0.9 – 1.3) 19 (8.5 – -93)
Kostroma Oblast 32 (19 – 42) Unsure 1.1 (0.8 – 1.3) 55 (8.4 – -12)
Krasnodar Krai 73 (56 – 87) Unsure 1 (0.8 – 1.1) -49 (23 – -12)
Krasnoyarsk Krai 161 (139 – 182) Unsure 1 (0.9 – 1.1) -150 (28 – -21)
Kursk Oblast 71 (55 – 86) Unsure 1 (0.9 – 1.2) 370 (15 – -16)
Leningrad Oblast 60 (45 – 74) Unsure 1.1 (0.9 – 1.2) 51 (11 – -19)
Lipetsk Oblast 61 (46 – 74) Unsure 1 (0.8 – 1.2) 5100 (14 – -14)
Magadan Oblast 18 (9 – 25) Unsure 1 (0.6 – 1.3) -82 (8.3 – -7)
Mari El Republic 48 (35 – 61) Unsure 1 (0.8 – 1.2) 82 (11 – -15)
Mordovia Republic 59 (42 – 71) Unsure 1 (0.9 – 1.2) 100 (12 – -16)
Moscow 1372 (1274 – 1466) Decreasing 0.9 (0.9 – 1) -37 (-98 – -22)
Moscow Oblast 726 (655 – 791) Unsure 1 (1 – 1.1) 280 (41 – -56)
Murmansk Oblast 67 (50 – 82) Unsure 1.1 (0.9 – 1.3) 42 (11 – -22)
Nizhny Novgorod Oblast 238 (206 – 269) Likely decreasing 0.9 (0.9 – 1) -37 (100 – -16)
North Ossetia - Alania Republic 35 (23 – 45) Unsure 0.9 (0.7 – 1.2) -55 (13 – -9.1)
Novgorod Oblast 57 (42 – 70) Unsure 1.1 (0.9 – 1.3) 36 (9.5 – -21)
Novosibirsk Oblast 108 (89 – 127) Unsure 1 (0.9 – 1.1) 280 (17 – -20)
Omsk Oblast 95 (76 – 112) Likely increasing 1.1 (1 – 1.3) 21 (9.4 – -77)
Orel Oblast 89 (73 – 106) Unsure 1 (0.8 – 1.1) -42 (30 – -12)
Orenburg Oblast 63 (46 – 76) Unsure 1 (0.8 – 1.2) -5200 (15 – -14)
Penza Oblast 90 (70 – 104) Unsure 0.9 (0.8 – 1.1) -32 (42 – -12)
Perm Krai 72 (54 – 85) Unsure 1 (0.9 – 1.2) 3100 (16 – -16)
Primorsky Krai 96 (77 – 112) Increasing 1.2 (1 – 1.3) 18 (8.7 – -290)
Pskov Oblast 81 (63 – 100) Likely increasing 1.1 (0.9 – 1.3) 28 (9.6 – -32)
Rostov Oblast 158 (134 – 181) Likely decreasing 0.9 (0.8 – 1) -31 (92 – -13)
Ryazan Oblast 54 (38 – 66) Likely decreasing 0.9 (0.8 – 1.1) -28 (25 – -9.2)
Saint Petersburg 259 (232 – 296) Likely decreasing 0.9 (0.8 – 1) -37 (150 – -17)
Sakha (Yakutiya) Republic 86 (67 – 100) Unsure 1.1 (0.9 – 1.2) 43 (12 – -26)
Sakhalin Oblast 25 (14 – 33) Unsure 1.2 (0.8 – 1.4) 25 (6.3 – -13)
Samara Oblast 87 (70 – 104) Unsure 1 (0.8 – 1.1) -120 (20 – -15)
Saratov Oblast 122 (99 – 139) Unsure 1 (0.9 – 1.2) 140 (17 – -24)
Sevastopol 6 (0 – 10) Likely increasing 1.7 (0.6 – 2.8) 9.9 (1.8 – -2.9)
Smolensk Oblast 71 (55 – 85) Decreasing 0.8 (0.7 – 0.9) -13 (-56 – -7.3)
Stavropol Krai 94 (73 – 109) Unsure 1 (0.9 – 1.2) 72 (14 – -23)
Sverdlovsk Oblast 262 (233 – 295) Unsure 1 (0.9 – 1.1) 170 (25 – -36)
Tambov Oblast 65 (51 – 80) Unsure 1 (0.8 – 1.2) -1100 (15 – -15)
Tatarstan Republic 44 (31 – 56) Unsure 0.9 (0.7 – 1.1) -37 (18 – -9.1)
Tomsk Oblast 64 (48 – 77) Likely increasing 1.1 (0.9 – 1.3) 23 (8.6 – -33)
Tula Oblast 95 (75 – 111) Unsure 1 (0.8 – 1.1) -59 (26 – -14)
Tver Oblast 75 (60 – 92) Unsure 1 (0.8 – 1.1) -50 (24 – -12)
Tyumen Oblast 58 (43 – 71) Likely increasing 1.2 (1 – 1.4) 17 (7.1 – -55)
Tyva Republic 116 (96 – 134) Unsure 1 (0.9 – 1.2) 280 (19 – -21)
Udmurt Republic 23 (14 – 32) Unsure 0.9 (0.7 – 1.2) -39 (10 – -6.7)
Ulyanovsk Oblast 134 (111 – 154) Likely increasing 1.1 (1 – 1.2) 24 (11 – -120)
Vladimir Oblast 60 (44 – 74) Unsure 1 (0.8 – 1.2) -1500 (14 – -14)
Volgograd Oblast 107 (87 – 123) Unsure 1 (0.9 – 1.1) -150 (22 – -17)
Vologda Oblast 26 (16 – 35) Unsure 1 (0.7 – 1.3) -230 (9.4 – -8.8)
Voronezh Oblast 209 (183 – 232) Unsure 1 (0.9 – 1.1) 250 (23 – -29)
Yamalo-Nenets Autonomous Okrug 106 (87 – 123) Likely increasing 1.2 (1 – 1.3) 20 (9.3 – -150)
Yaroslavl Oblast 51 (37 – 64) Likely decreasing 0.9 (0.7 – 1.1) -25 (27 – -8.5)
Zabaykalsky Krai 80 (62 – 96) Unsure 1.1 (0.9 – 1.2) 84 (13 – -19)

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Mironov, Sergey. 2020. “COVID-19 Data from Jhu Csse, Updated with Details on Russian Regions.” Github Repository. https://github.com/grwlf/COVID-19_plus_Russia.

Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.